• DocumentCode
    2126747
  • Title

    Keystroke Biometric Studies on Password and Numeric Keypad Input

  • Author

    Bakelman, Ned ; Monaco, John V. ; Sung-Hyuk Cha ; Tappert, Charles C.

  • Author_Institution
    Seidenberg Sch. of Comput. Sci. & Inf. Syst., Pace Univ., White Plains, NY, USA
  • fYear
    2013
  • fDate
    12-14 Aug. 2013
  • Firstpage
    204
  • Lastpage
    207
  • Abstract
    The keystroke biometric classification system described in this study was evaluated on two types of short input - passwords and numeric keypad input. On the password input, the system outperforms 14 other systems evaluated in a previous study using the same raw input data. The three top performing systems in that study had equal error rates between 9.6% and 10.2%. With the classification system developed in this study, equal error rates of 8.7% were achieved on both the features from the previous study and on a new set of features. On the numeric keypad input, the system achieved an equal error rate of 10.5% on the features from the previous study and 6.1% on a new set of features.
  • Keywords
    biometrics (access control); message authentication; pattern classification; equal error rates; keystroke biometric classification system; keystroke biometric studies; numeric keypad input; password input; Authentication; Biometrics (access control); Educational institutions; Error analysis; Sociology; Statistics; biometrics; keystroke biometrics; machine learning; pattern recognition; user authentication;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligence and Security Informatics Conference (EISIC), 2013 European
  • Conference_Location
    Uppsala
  • Type

    conf

  • DOI
    10.1109/EISIC.2013.45
  • Filename
    6657155